The DARPA PerceptOR program has implemented a rigorous evaluative test program which fosters the development of field relevant outdoor mobile robots. Autonomous ground vehicles were deployed on diverse test courses throughout the USA and quantitatively evaluated on such factors as autonomy level, waypoint acquisition, failure rate, speed, and communications bandwidth. Our efforts over the three year program have produced new approaches in planning, perception, localization, and control which have been driven by the quest for reliable operation in challenging environments. This paper focuses on some of the most unique aspects of the systems developed by the CMU PerceptOR team, the lessons learned during the effort, and the most immediate challenges that remain to be addressed.

This paper describes a correlation-based, iterative, multi-resolution algorithm which estimates both scene structure and the motion of the camera rig through an environment from the stream(s) of incoming images. Both single-camera rigs and multiple-camera rigs can be accommodated. The use of multiple synchronized cameras results in more rapid convergence of the iterative approach. The algorithm uses a global ego-motion constraint to refine estimates of inter-frame camera rotation and translation. It uses local window-based correlation to refine the current estimate of scene structure. All analysis is performed at multiple resolutions.In order to combine, in a straightforward way, the correlation surfaces from multiple viewpoints and from multiple pixels in a support region, each pixel's correlation surface is modeled as a quadratic. This parameterization allows direct, explicit computation of incremental refinements for ego-motion and structure using linear algebra. Batches can be of arbitrary size, allowing a trade-off between accuracy and latency. Batches can also be daisychained for extended sequences. Results of the algorithm are shown on synthetic and real outdoor image sequences.

Purpose -The objective of this exploratory study is to investigate the "flow-through" or relationship between top-line measures of hotel operating performance (occupancy, average daily rate and revenue per available room) and bottom-line measures of profitability (gross operating profit and net operating income), before and during the recent great recession.Design/methodology/approach -This study uses data provided by PKF Hospitality Research for the period from 2007-2009. A total of 714 hotels were analyzed and various top-line and bottom-line profitability changes were computed using both absolute levels and percentages. Multiple regression analysis was used to examine the relationship between top and bottom line measures, and to derive flow-through ratios.Findings -The results show that average daily rate (ADR) and occupancy are significantly and positively related to gross operating profit per available room (GOPPAR) and net operating income per available room (NOIPAR). The evidence indicates that ADR, rather than occupancy, appears to be the stronger predictor and better measure of RevPAR growth and bottom-line profitability. The correlations and explained variances are also higher than those reported in prior research. Flow-through ratios range between 1.83 and 1.91 for NOIPAR, and between 1.55 and 1.65 for GOPPAR, across all chain-scales.Research limitations/implications -Limitations of this study include the limited number of years in the study period, limited number of hotels in a competitive set, and self-selection of hotels by the researchers.Practical implications -While ADR and occupancy work in combination to drive profitability, the authors' study shows that ADR is the stronger predictor of profitability. Hotel managers can use flow-through ratios to make financial forecasts, or use them as inputs in valuation models, to forecast future profitability.Originality/value -This paper extends prior research on the relationship between top-line measures and bottom-line profitability and serves to inform lodging owners, operators and asset managers about flowthrough ratios, and how these ratios impact hotel profitability.

We use Sarnoff 's next-generation video processor, the PVT-200, to demonstrate real-time algorithms for stereo processing, obstacle detection, and terrain estimation from stereo cameras mounted on a moving vehicle. Sarnoff 's stereo processing and obstacle detection capabilities are currently being used in several Unmanned Ground Vehicle (UGV) programs, including MDARS-E and DEMO III. Sarnoff 's terrain estimation capabilities are founded on a "model-based directed stereo" approach. We demonstrate ongoing collaborative research between Sarnoff and Universitat der Bundeswehr Miinchen, where we are studying vision processing for autonomous off-road navigation as part of the AUTONAVprogram. Comments Copyright 1998 IEEE. Reprinted from Proceedings of the 4th IEEE Workshop on the Applications of ComputerVision, October 1998, pages 288-289.This material is posted her with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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